OpenRaiser/Intern-Atlas huggingface dataset:
Coverability: Realizability Lower Bounds
Venue: arXiv cs.LO 2018
Year: 2018

We introduce the problem of temporal coverability for realizability and synthesis. Namely, given a language of words that must be covered by a produced system, how to automatically produce such a system. We consider the case of coverability with no further specifications, where we have to show that the nondeterminism of the produced system is sufficient to produce all the words required in the output language. We show a counting argument on a deterministic automaton representing the language to be covered that allows to produce such a system. We then turn to the case of coverability with additional specification and give a precondition for the existence of a system that produces all required words and at the same time produces only computations satisfying the additional correctness criterion. We combine our counting argument on the deterministic automaton for the language to be covered with a ranking on the deterministic Büchi automaton for the correctness criterion. One of the major issues with practical realizability is the interaction between environment assumptions and system guarantees. In many cases, synthesis produces systems that are vacuous and concentrate on forcing the environment to falsify its assumptions instead of fulfilling their guarantees. Coverability offers an alternative approach to tackle this problem.
The Effect of Pets on Happiness: A Large-scale Multi-Factor Analysis using Social Multimedia
Venue: arXiv cs.CY 2018
Year: 2018

From reducing stress and loneliness, to boosting productivity and overall well-being, pets are believed to play a significant role in people's daily lives. Many traditional studies have identified that frequent interactions with pets could make individuals become healthier and more optimistic, and ultimately enjoy a happier life. However, most of those studies are not only restricted in scale, but also may carry biases by using subjective self-reports, interviews, and questionnaires as the major approaches. In this paper, we leverage large-scale data collected from social media and the state-of-the-art deep learning technologies to study this phenomenon in depth and breadth. Our study includes four major steps: 1) collecting timeline posts from around 20,000 Instagram users, 2) using face detection and recognition on 2-million photos to infer users' demographics, relationship status, and whether having children, 3) analyzing a user's degree of happiness based on images and captions via smiling classification and textual sentiment analysis, 3) applying transfer learning techniques to retrain the final layer of the Inception v3 model for pet classification, and 4) analyzing the effects of pets on happiness in terms of multiple factors of user demographics. Our main results have demonstrated the efficacy of our proposed method with many new insights. We believe this method is also applicable to other domains as a scalable, efficient, and effective methodology for modeling and analyzing social behaviors and psychological well-being. In addition, to facilitate the research involving human faces, we also release our dataset of 700K analyzed faces.
The Challenges in SDN/ML Based Network Security : A Survey
Venue: arXiv cs.CR 2018
Year: 2018

Machine Learning is gaining popularity in the network security domain as many more network-enabled devices get connected, as malicious activities become stealthier, and as new technologies like Software Defined Networking (SDN) emerge. Sitting at the application layer and communicating with the control layer, machine learning based SDN security models exercise a huge influence on the routing/switching of the entire SDN. Compromising the models is consequently a very desirable goal. Previous surveys have been done on either adversarial machine learning or the general vulnerabilities of SDNs but not both. Through examination of the latest ML-based SDN security applications and a good look at ML/SDN specific vulnerabilities accompanied by common attack methods on ML, this paper serves as a unique survey, making a case for more secure development processes of ML-based SDN security applications.
ConPredictor: Concurrency Defect Prediction in Real-World Applications
Venue: arXiv cs.SE 2018
Year: 2018

Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world programs. Software defect prediction has been used to help developers find faults and prioritize their testing efforts. Prior studies have used machine learning to build such predicting models based on designed features that encode the characteristics of programs. However, research has focused on sequential programs; to date, no work has considered defect prediction for concurrent programs, with program characteristics distinguished from sequential programs. In this paper, we present ConPredictor, an approach to predict defects specific to concurrent programs by combining both static and dynamic program metrics. Specifically, we propose a set of novel static code metrics based on the unique properties of concurrent programs. We also leverage additional guidance from dynamic metrics constructed based on mutation analysis. Our evaluation on four large open source projects shows that ConPredictor improved both within-project defect prediction and cross-project defect prediction compared to traditional features.
Binary Space Partitioning as Intrinsic Reward
Venue: arXiv cs.AI 2018
Year: 2018

An autonomous agent embodied in a humanoid robot, in order to learn from the overwhelming flow of raw and noisy sensory, has to effectively reduce the high spatial-temporal data dimensionality. In this paper we propose a novel method of unsupervised feature extraction and selection with binary space partitioning, followed by a computation of information gain that is interpreted as intrinsic reward, then applied as immediate-reward signal for the reinforcement-learning. The space partitioning is executed by tiny codelets running on a simulated Turing Machine. The features are represented by concept nodes arranged in a hierarchy, in which those of a lower level become the input vectors of a higher level.
DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
Venue: arXiv cs.CR 2018
Year: 2018

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework.
Report on the 7th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2018)
Venue: arXiv cs.IR 2018
Year: 2018

The Bibliometric-enhanced Information Retrieval (BIR) workshop series has started at ECIR in 2014 and serves as the annual gathering of IR researchers who address various information-related tasks on scientific corpora and bibliometrics. We welcome contributions elaborating on dedicated IR systems, as well as studies revealing original characteristics on how scientific knowledge is created, communicated, and used. This report presents all accepted papers at the 7th BIR workshop at ECIR 2018 in Grenoble, France.
Design and Control of an Aerial Manipulator for Contact-based Inspection
Venue: arXiv cs.RO 2018
Year: 2018

Manipulator dynamics, external forces and moments raise issues in stability and efficient control during aerial manipulation. Additionally, multirotor Micro Aerial Vehicles impose stringent limits on payload, actuation and system states. In view of these challenges, this work addressed the design and control of a 3-DoF serial aerial manipulator for contact inspection. A lightweight design with sufficient dexterous workspace for NDT (Non-Destructive Testing) inspection is presented. This operation requires the regulation of normal force on the inspected point. Contact dynamics have been discussed along with a simulation of the closed-loop dynamics during contact. The simulated controller preserves inherent system nonlinearities and uses a passivity approach to ensure the convergence of error to zero. A transition scheme from free-flight to contact was developed along with the hardware and software frameworks for implementation. This paper concludes with important drawbacks and prospects.
Manipulation-resistant facility location mechanisms for ZV-line graphs
Venue: arXiv cs.GT 2018
Year: 2018

In many real-life scenarios, a group of agents needs to agree on a common action, e.g., on the location for a public facility, while there is some consistency between their preferences, e.g., all preferences are derived from a common metric space. The facility location problem models such scenarios and it is a well-studied problem in social choice. We study mechanisms for facility location on graphs, which are resistant to manipulations (strategy-proof, abstention-proof, and false-name-proof) by both individuals and coalitions and are efficient (Pareto optimal). We define a family of graphs, ZV-line graphs, and show a general facility location mechanism for these graphs which satisfies all these desired properties. Moreover, we show that this mechanism can be computed in polynomial time, the mechanism is anonymous, and it can equivalently be defined as the first Pareto optimal location according to some predefined order. Our main result, the ZV-line graphs family and the mechanism we present for it, unifies the few current works in the literature of false-name-proof facility location on discrete graphs, including all the preliminary (unpublished) works we are aware of. Finally, we discuss some generalizations and limitations of our result for problems of facility location on other structures.
Network Structure, Efficiency, and Performance in WikiProjects
Venue: arXiv cs.SI 2018
Year: 2018

The internet has enabled collaborations at a scale never before possible, but the best practices for organizing such large collaborations are still not clear. Wikipedia is a visible and successful example of such a collaboration which might offer insight into what makes large-scale, decentralized collaborations successful. We analyze the relationship between the structural properties of WikiProject coeditor networks and the performance and efficiency of those projects. We confirm the existence of an overall performance-efficiency trade-off, while observing that some projects are higher than others in both performance and efficiency, suggesting the existence factors correlating positively with both. Namely, we find an association between low-degree coeditor networks and both high performance and high efficiency. We also confirm results seen in previous numerical and small-scale lab studies: higher performance with less skewed node distributions, and higher performance with shorter path lengths. We use agent-based models to explore possible mechanisms for degree-dependent performance and efficiency. We present a novel local-majority learning strategy designed to satisfy properties of real-world collaborations. The local-majority strategy as well as a localized conformity-based strategy both show degree-dependent performance and efficiency, but in opposite directions, suggesting that these factors depend on both network structure and learning strategy. Our results suggest possible benefits to decentralized collaborations made of smaller, more tightly-knit teams, and that these benefits may be modulated by the particular learning strategies in use.
Differentially Private Confidence Intervals for Empirical Risk Minimization
Venue: arXiv cs.LG 2018
Year: 2018

The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the problem of designing confidence intervals for the parameters of a variety of differentially private machine learning models. The algorithms can provide confidence intervals that satisfy differential privacy (as well as the more recently proposed concentrated differential privacy) and can be used with existing differentially private mechanisms that train models using objective perturbation and output perturbation.
Enumerating All Subgraphs without Forbidden Induced Subgraphs via Multivalued Decision Diagrams
Venue: arXiv cs.DS 2018
Year: 2018

We propose a general method performed over multivalued decision diagrams that enumerates all subgraphs of an input graph that are characterized by input forbidden induced subgraphs. Our method combines elaborations of classical set operations and the developing construction technique, called the frontier based search, for multivalued decision diagrams. Using the algorithm, we enumerated all the chordal graphs of size at most 10 on multivalued decision diagrams.
AFA-PredNet: The action modulation within predictive coding
Venue: arXiv cs.RO 2018
Year: 2018

The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We propose a neural architecture in which such regularities of active inference are encoded hierarchically. We further suggest that this encoding emerges during the embodied learning process when the appropriate action is selected to minimize the prediction error in perception. Therefore, this predictive stream in the sensorimotor loop is generated in a top-down manner. Specifically, it is constantly modulated by the motor actions and is updated by the bottom-up prediction error signals. In this way, the top-down prediction originally comes from the prior experience from both perception and action representing the higher levels of this hierarchical cognition. In our proposed embodied model, we extend the PredNet Network, a hierarchical predictive coding network, with the motor action units implemented by a multi-layer perceptron network (MLP) to modulate the network top-down prediction. Two experiments, a minimalistic world experiment, and a mobile robot experiment are conducted to evaluate the proposed model in a qualitative way. In the neural representation, it can be observed that the causal inference of predictive percept from motor actions can be also observed while the agent is interacting with the environment.
Analyzing the activities of visitors of the Leiden Ranking website
Venue: arXiv cs.DL 2018
Year: 2018

To provide a better understanding of the way in which university rankings are used, we present a detailed analysis of the activities of visitors of a university ranking website. We use the website of the CWTS Leiden Ranking for this purpose. We for instance study the countries from which visitors originate, the specific pages on the Leiden Ranking website that they visit, the countries or the universities that they find of special interest, and the indicators that they focus on. In addition, we also discuss two experiments that were carried out on the Leiden Ranking website. Our analysis does not only provide new insights into the use of university rankings, but it also suggests possible ways in which these rankings can be improved.
A synopsis of comparative metrics for classifications
Venue: arXiv cs.DS 2018
Year: 2018

Phylogeny is the study of the relations between biological entities. From it, the need to compare tree-like graphs has risen and several metrics were established and researched, but since there is no definitive way to compare them, its discussion is still open nowadays. All of them emphasize different features of the structures and, of course, the efficiency of these computations also varies. The work in this article is mainly expositive (a lifting from a collection of papers and articles) with special care in its presentation (trying to mathematically formalize what was not presented that way previously) and filling (with original work) where information was not available (or at least, to our knowledge) given the frame we set to fit these metrics, which was to state their discriminative power and time complexity. The Robinson Foulds, Robinson Foulds Length, Quartet, Triplet, Triplet Length, Geodesic metrics are approached with greater detail (stating also some of its problems in formulation and discussing its intricacies) but the reader can also expect that less used (but not necessarily less important or less promising) metrics will be covered, which are Maximum Aggreement Subtree, Align, Cophenetic Correlation Coeficcient, Node, Similarity Based on Probability, Hybridization Number and Subtree Prune and Regraft. Finally, some challenges that sprouted from making this synopsys are presented as a possible subject of study and research.
An Estimation of Favorite Value in Emotion Generating Calculation by Fuzzy Petri Net
Venue: arXiv cs.AI 2018
Year: 2018

Emotion Generating Calculations (EGC) method based on the Emotion Eliciting Condition Theory can decide whether an event arouses pleasure or not and quantify the degree under the event. An event in the form of Case Frame representation is classified into 12 types of calculations. However, the weak point in EGC is Favorite Value (FV) as the personal taste information. In order to improve the problem, this paper challenges to establish a learning method to learn speaker's taste information from dialog. Especially, the learning method employs Fuzzy Petri Net to find an appropriate FV to a word which has the unknown FV. This paper discusses the effective learning method to improve a weak point of EGC when a missing value of FV exists.
Assimilation of fire perimeters and satellite detections by minimization of the residual in a fire spread model
Venue: arXiv cs.CE 2018
Year: 2018

Assimilation of data into a fire-spread model is formulated as an optimization problem. The level set equation, which relates the fire arrival time and the rate of spread, is allowed to be satisfied only approximately, and we minimize a norm of the residual. Previous methods based on modification of the fire arrival time either used an additive correction to the fire arrival time, or made a position correction. Unlike additive fire arrival time corrections, the new method respects the dependence of the fire rate of spread on diurnal changes of fuel moisture and on weather changes, and, unlike position corrections, it respects the dependence of the fire spread on fuels and terrain as well. The method is used to interpolate the fire arrival time between two perimeters by imposing the fire arrival time at the perimeters as constraints.
On Geodesically Convex Formulations for the Brascamp-Lieb Constant
Venue: arXiv cs.DS 2018
Year: 2018

We consider two non-convex formulations for computing the optimal constant in the Brascamp-Lieb inequality corresponding to a given datum, and show that they are geodesically log-concave on the manifold of positive definite matrices endowed with the Riemannian metric corresponding to the Hessian of the log-determinant function. The first formulation is present in the work of Lieb and the second is inspired by the work of Bennett et al. Recent works of Garg et al.and Allen-Zhu et al. also imply a geodesically log-concave formulation of the Brascamp-Lieb constant through a reduction to the operator scaling problem. However, the dimension of the arising optimization problem in their reduction depends exponentially on the number of bits needed to describe the Brascamp-Lieb datum. The formulations presented here have dimensions that are polynomial in the bit complexity of the input datum.
Analyzing Self-Driving Cars on Twitter
Venue: arXiv cs.LG 2018
Year: 2018

This paper studies users' perception regarding a controversial product, namely self-driving (autonomous) cars. To find people's opinion regarding this new technology, we used an annotated Twitter dataset, and extracted the topics in positive and negative tweets using an unsupervised, probabilistic model known as topic modeling. We later used the topics, as well as linguist and Twitter specific features to classify the sentiment of the tweets. Regarding the opinions, the result of our analysis shows that people are optimistic and excited about the future technology, but at the same time they find it dangerous and not reliable. For the classification task, we found Twitter specific features, such as hashtags as well as linguistic features such as emphatic words among top attributes in classifying the sentiment of the tweets.
ISIS at its apogee: the Arabic discourse on Twitter and what we can learn from that about ISIS support and Foreign Fighters
Venue: arXiv cs.CL 2018
Year: 2018

We analyze 26.2 million comments published in Arabic language on Twitter, from July 2014 to January 2015, when ISIS' strength reached its peak and the group was prominently expanding the territorial area under its control. By doing that, we are able to measure the share of support and aversion toward the Islamic State within the online Arab communities. We then investigate two specific topics. First, by exploiting the time-granularity of the tweets, we link the opinions with daily events to understand the main determinants of the changing trend in support toward ISIS. Second, by taking advantage of the geographical locations of tweets, we explore the relationship between online opinions across countries and the number of foreign fighters joining ISIS.
Ransomware Payments in the Bitcoin Ecosystem
Venue: arXiv cs.CR 2018
Year: 2018

Ransomware can prevent a user from accessing a device and its files until a ransom is paid to the attacker, most frequently in Bitcoin. With over 500 known ransomware families, it has become one of the dominant cybercrime threats for law enforcement, security professionals and the public. However, a more comprehensive, evidence-based picture on the global direct financial impact of ransomware attacks is still missing. In this paper, we present a data-driven method for identifying and gathering information on Bitcoin transactions related to illicit activity based on footprints left on the public Bitcoin blockchain. We implement this method on-top-of the GraphSense open-source platform and apply it to empirically analyze transactions related to 35 ransomware families. We estimate the lower bound direct financial impact of each ransomware family and find that, from 2013 to mid-2017, the market for ransomware payments has a minimum worth of USD 12,768,536 (22,967.54 BTC). We also find that the market is highly skewed with only a few number of players responsible for the majority of the payments. Based on these research findings, policy-makers and law enforcement agencies can use the statistics provided to understand the size of the illicit market and make informed decisions on how best to address the threat.